The rapid development of video analysis technologies in recent years has made the smart video surveillance an important issue in security. One common surveillance issue is that the surveillance camera may be subject to sabotage or tampering in certain way to change the captured views, such as, moving the camera lens to change the shooting angle, spraying paints to the camera lens, changing the focus or the ambient lighting source, and so on. All the above changes will severely damage the surveillance quality. Therefore, if the tampering can be effectively detected and the message of tampering detection can be passed to related surveillance personnel, the overall effectiveness of the surveillance systems may be greatly enhanced. Hence, how to detect camera tampering event and transmitting tampering information has become an important issue faced by smart surveillance application.
The video surveillance system currently available in the market may be roughly categorized as analog transmission surveillance based on analog camera with digital video recorder (DVR), and digital network surveillance based on network camera with network video recorder (NVR). According to the survey by IMS Research on the market size in 2007, the total shipment amounts of analog cameras, network camera, DVR and NVR are 13838000, 1199000, 1904000 and 38000 sets, respectively. In 2012, the market is expected to grow to 24236000, 6157000, 5184000, and 332000 sets, respectively. From the above industrial information, the analog transmission surveillance is still expected to stay as the mainstream of the surveillance market for the next several years. In addition, the users currently using analog transmission surveillance solutions are unlikely to replace the current systems. Therefore, the analog transmission surveillance will be difficult to be replaced in the next several years. On the other hand, the digital network surveillance system may also grow steadily. Therefore, how to cover both analog transmission surveillance and digital network surveillance solutions remains a major challenge to the video surveillance industry.
The majority of current camera tampering systems focus on the sabotage detection of the camera. That is, the detection of camera sabotage is based on the captured image. These systems can be classified as transmitting-end detection or receiving-end detection. FIG. 1 shows a schematic view of transmitting-end detection system. As shown in FIG. 1, transmitting-end detection system will relay the video image signal from the camera for camera sabotage detection, store the sabotage detection result to a front-end storage medium, and provide a server for inquiry (usually a web server). In this case, the receiving-end needs to inquire the sabotage result information in addition to receiving video images so as to display the sabotage information to the user. The problem of this type of deployment is that the detection signal and the video image are transmitted separately, and will incur additional routing and deployment costs. FIG. 2 shows a schematic view of receiving-end detection system. As shown in FIG. 2, the receiving-end detection system transmits the video signal to the receiving-end and then performs the camera sabotage detection. In this manner, the receiving-end usually must be capable of processing video inputs from a plurality of cameras and performing user interface operation, display, storing and sabotage detection. Therefore, the hardware requirement for the receiving-end is higher and usually needs a high computing-power computer.
Taiwan Publication No. 200830223 disclosed a method and module for identifying the possible tampering on cameras. The method includes the steps of: receiving an image for analysis from an image sequence; transforming the received image into an edge image; generating a similarity index indicating the similarity between the edge image and a reference edge image; and if the similarity index is within a defined range, the camera may be tampered. This method uses the comparison of two edge images for statistical analysis as a basis for identifying the possible camera tampering. Therefore, the effectiveness is limited.
U.S. Publication No. US2007/0247526 disclosed a camera tamper detection based on image comparison and moving object detection. The method emphasizes the comparison between current captured image and the reference image, without feature extraction and construction of integrated features.
U.S. Publication No. US2007/0126869 disclosed a system and method for automatic camera health monitoring, i.e., a camera malfunction detection system based on health records. The method stores the average frame, average energy and anchor region information as the health record, and compares the current health record against the stored records. When the difference reaches a defined threshold, the tally counter is incremented. When the tally counter reaches a defined threshold, the system is identified as malfunctioning. The method is mainly applied for malfunction determination, and is the same as Taiwan Publication No. 200830223, with limited effectiveness.
As aforementioned, the surveillance systems available in the market usually transmit the image information and change information through different channels. If the user needs to know the accurate change information, the user usually needs to use the software development kit (SDK) corresponding to the devices of the system. When an event occurs, some surveillance systems will display some visual warning effect, such as, flashing by displaying an image and a full-white image alternatingly, or adding a red frame on the image. However, all these visual effects are only for warning purpose. When the smart analysis is performed at the front-end device, the back-end device is only warned of the event, instead of knowing the judgment basis or reusing the computed result to avoid the computing resource waste and improve the efficiency.
Furthermore, as a surveillance system is often deployed in phases. Therefore, the final surveillance system may include surveillance devices from different manufacturers with vastly different interfaces. In addition, as the final surveillance system grows larger in scale, more and more smart devices and cameras will be connected. If all these smart devices must repeat the analysis and computing that other smart devices have done, the waste would be tremendous. As video image is an essential part of the surveillance system planning and deployment, most of the devices will deal with video transmission interface. If the video analysis information can be obtained through the video channel to enhance or facilitate the subsequent analysis via reusing prior analysis information and highlighted graphic display is used to inform the user of the event, the flexibility of the surveillance system can be vastly improved.